The ultimate goal of this project is to identify age-related changes in bone in the context of other metabolically active tissues. Age-related decline in bone strength increases risk of fractures which occur in 1 in 2 American women and 1 in 4 American men after age 45. Our current methods of identifying individuals at risk of fracture rely on bone mineral density. However, 85% of individuals with a fracture do not have bone mineral density low enough to be clinically diagnosed with osteoporosis so other contributors to age-related bone fracture risk must be identified and targeted with new treatment options. This project examines age-related differences in DNA sequence, gene expression (?transcriptomics?), and protein expression (?proteomics?) of trabecular bone in baboons to test the hypothesis that an integrated ?omics? approach will identify more dysregulated pathways in elderly individuals than individual analyses alone. Because of its strong similarity to humans in bone turnover, age-related decline, and metabolism, the baboon will be used as a model for human bone aging. Aim 1: Identify changes in protein interaction networks associated with transition out of middle-age bone homeostasis to elderly bone decline. Network analyses will identify pathways that change with age and by sex. Aim 2: Evaluate utility of multi-omic integration of genomic, transcriptomic, and proteomic data in describing biological changes in bone driven by aging. Integrated analyses will differentiate between network dysregulation arising from simultaneous age-related changes at multiple levels of cellular organization or inter-regulation among the levels versus pathologies rooted solely in proteomic variation. Aim 3: Differentiate between age- related changes in integrated biological pathways occurring within the musculoskeletal system versus those occurring more broadly. Differentially integrated omic pathways between middle-aged and older animals will be compared between bone and skeletal muscle, which are functionally integrated, and between bone and liver, which are both metabolically responsive tissues. Through the accomplishment of these significant aims, this project will train the PI in a new areas of omic analysis (proteomics) which she will integrate with previous experience in the statistical analysis of genomic, transcriptomic, and epigenomic data. This training will including coursework in aging biology and workshops in proteomic, metabolomic, and integrative omics analysis plus extended hands-on training under the direction of her mentors. Demand for these types of integrative omics skills is increasing in our current ?big data? era. The PI will develop the skills and preliminary data necessary to compete for independent NIH funding to study the biology of bone aging in the context of the metabolic syndrome. Ultimately, this will lead to the success of her long term goal of utilizing multi-omic technologies to identify the earliest declines in bone health and fracture risk and determining which biological pathways can be targeted for intervention to prevent fracture and preserve health and vitality among older adults.